ArticleFrontiers in aging neuroscience2023
An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease.
Article in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.Brain informatics · 2026Review
- AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques.Global epidemiology · 2026Article
- A Brain Connectivity Approach to Detect Diffusion-Weighted Imaging Changes in Post-Traumatic Epilepsy.Bioengineering (Basel, Switzerland) · 2026Article
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Alzheimer's disease prediction via an explainable CNN using genetic algorithm and SHAP values.PloS one · 2026Article
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- A comprehensive interpretable machine learning framework for mild cognitive impairment and Alzheimer's disease diagnosis.Scientific reports · 2025Article
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Diagnostic classification of mild cognitive impairment in Parkinson's disease using subject-level stratified machine-learning analysis.Frontiers in aging neuroscience · 2025Article
- Characterizing dynamic functional connectivity subnetwork contributions in narrative classification with Shapley values.Network neuroscience (Cambridge, Mass.) · 2025Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Deep Ensemble learning and quantum machine learning approach for Alzheimer's disease detection.Scientific reports · 2024Article
- Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach.Frontiers in public health · 2024Article
- Explainable machine learning radiomics model for Primary Progressive Aphasia classification.Frontiers in systems neuroscience · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.
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